Method and system for proactive fraudster exposure in a customer service channel

ABSTRACT

A computer-implemented method for proactive fraudster exposure in a customer service center having multiple service channels is provided herein. The computer-implemented method may collect call interactions based on predefined rules by a calls collection engine and store the collected call interactions. The computer-implemented method may further analyze the call interactions by a Proactive Fraud Exposure engine by: (i) generating a voiceprint for each call interaction; (ii) using machine learning technique to group the call interactions into one or more clusters based on respective voiceprints in the voiceprints database; (iii) storing the one or more clusters; and (iv) ranking and classifying the one or more clusters to yield a list of potential fraudsters. The computer-implemented method may further transmit the list of potential fraudsters to a user to enable the user to review said list of potential fraudsters and to add fraudsters from the list to a watchlist database.

TECHNICAL FIELD

The present disclosure relates to the field of voice biometric securityand real-time authentication, and more specifically to method and systemfor proactive fraudster exposure in a customer service channel byfraudsters clustering and displaying to a user a ranked list ofpotential fraudsters to add to a watchlist database.

BACKGROUND

Call centers are increasingly becoming a target for fraudsters via theircustomer service channels. Call center frauds are one of the leadingthreats that organizations such as financial institutions face.Fraudsters commonly attempt to retrieve information or changeinformation of other legitimate customers by exploiting call centeragents by social engineering. For example, fraudsters may conduct anattack on a financial institution by manipulating the call center agentsto provide them with confidential information of legitimate customersand then use the extracted information to commit another fraud e.g.,identity theft. Instead of social engineering, fraudsters may useinformation from social networks or public information to correctlyanswer knowledge-based questions during a call with an agent.

Fraudulent activity may take many shapes and forms. It may be performedvia multiple frequent attacks or attempts on a singular legitimatecustomer account or on multiple customer accounts. The attacks may bevia different channels such as mobile application, call-center calls orinternet on different lines of business e.g., VIP handling agents.Another type of attack is a “targeted attack” in which the attack istargeted to a specific individual i.e., customer. Yet, another type ofattack is “spread out attack” in which the attack is on variouscustomers in the call center.

Currently, one practice to mitigate the threats to the call center ishaving a fraud team including a few security officers. These fewsecurity officers are responsible to make sure that the customers datais protected by investigating fraudulent behavior with their existingtools or following customers complaints and handling those scenarios.However, listening to a large amount of call interactions of thousandsof agents which respond to abundance calls per day, might beinefficient. Also, these security officers struggle to detect most ofthe fraudulent activities and fraudsters and add the detected fraudstersto their known fraudsters list but, this practice does not providecoverage for unknown fraudsters which are not in the known fraudsterslist.

Furthermore, the implementation of current practices maintains the callcenters exposed to fraudsters. The sample of random calls, out of theplethora of calls, that is checked by the few security officers mayoverlook some of the fraudsters. Therefore, there is a need for aproactive fraudster exposure system and method that will analyze the bigdata of call interactions and extract information related to fraudsters,to be later on presented to security officers, so they will add thefraudsters to a watchlist, so that in the future they could be blocked,in real-time.

Currently, there is no solution that provides the ability toautomatically detect new fraudsters by analysis of varied andhigh-volume call interactions which are occurring in high velocitytogether with biometric authentication technique such as voicesignature, in real-time. Furthermore, currently there is no solutionthat does not require any manual pre-setup or pre-sorting of audiocalls.

SUMMARY

There is thus provided, in accordance with some embodiments of thepresent disclosure, a method for proactive fraudster exposure in acustomer service center having multiple service channels.

In accordance with some embodiments of the present disclosure, thecomputer-implemented method comprising: (a) collecting call interactionsfrom a database of recorded calls in a customer service channel. Thecollecting is based on predefined rules by a calls collection engine;(b) storing the collected call interactions in an interactions database;(c) analyzing the call interactions in the interactions database by aProactive Fraud Exposure (PFE) engine, said analyzing comprising: (i)generating a voiceprint for each call interaction in the interactionsdatabase to be stored in a voiceprints database; (ii) using machinelearning technique to group the call interactions in the interactiondatabase into one or more clusters based on respective voiceprints inthe voiceprints database. Each one of the one or more clusters isassociated with a repeating speaker's voice based on the generatedvoiceprints; (iii) storing the one or more clusters in a clustersdatabase; and (iv) ranking and classifying the one or more clustersstored in the clusters database to yield a list of potential fraudsters,and (d) transmitting the list of potential fraudsters to an applicationto display to a user the list of potential fraudsters via a displayunit, thus enabling said user to review said list of potentialfraudsters and to add fraudsters from the list to a watchlist database.

Furthermore, in accordance with some embodiments of the presentdisclosure, the generating of voiceprints is performed by extractingi-vectors which represents a speaker effect and a channel effect.

Furthermore, in accordance with some embodiments of the presentdisclosure, the method further comprising detecting fraudsters which arestored on the watchlist database in new call interactions to thecustomer service center via one of the multiple service channels, inreal-time.

There is further provided, in accordance with some embodiments of thepresent disclosure, the ranking is performed by at least one of: (i)inter-cluster statistics; and (ii) probability of representing afraudster or any combination thereof.

Furthermore, in accordance with some embodiments of the presentdisclosure, the probability of representing a fraudster is calculatedbased on at least one of the following factors: (i) same voice on sameclaimed customer; (ii) same voice on different claimed customers; (iii)fraudulent behavioral characteristics of the call interaction,manifested in the voice; (iv) metadata representing details of apredefined line of business.

Furthermore, in accordance with some embodiments of the presentdisclosure, the method further comprising attributing a predefinedweight value to the factors and the wherein the ranking is further basedon a weighted average of the factors.

Furthermore, in accordance with some embodiments of the presentdisclosure, the predefined rules are at least one of: (i) mismatchduring customer authentication; (ii) business data; (iii) agents thatare associated with a risk group or line of business; (iv) behavioralflows of the speaker; (v) call content analysis; and (vi) frequency ofthe call interactions or any combination thereof.

Furthermore, in accordance with some embodiments of the presentdisclosure, the analyzing is performed on audio or textual content.

Furthermore, in accordance with some embodiments of the presentdisclosure, the collecting is further based on automatedmachine-learning algorithms, such as phonetic speech and voice analysis.

Furthermore, in accordance with some embodiments of the presentdisclosure, the ranking further includes: (i) comparing each callinteraction in the interaction database to all other call interactionsin the call interaction database to yield a matrix of comparisons; (ii)scoring each pair of call interactions based on the extracted i-vectors;(iii) retrieving from each row in the matrix of comparisons a pair ofcall interactions (i,j) with the higher score; and (iv) for eachretrieved pair of call interactions (i,j) perform clustering.

Furthermore, in accordance with some embodiments of the presentdisclosure, the clustering is performed according to the followingconditions: when the score of the pair of call interactions (i,j) ishigher than a predefined threshold: a. when both call interactions (i,j)were not assigned to a cluster, assign both interactions to a newcluster; b. when only one of the call interactions (i,j) is assigned toa cluster add the call interaction that is not assigned to the cluster;c. when both call interactions are assigned merge them to one cluster;when the score of the pair of call interactions (i,j) is not higher thana predefined threshold: call interaction (i) is assigned to a newcluster. Call interaction (i) has the highest score in a row.

Furthermore, in accordance with some embodiments of the presentdisclosure, the classifying comprises calculating a confidence value foreach cluster based on the inner ties between the call interactions inthe cluster.

There is further provided, in accordance with some embodiments of thepresent disclosure, a computerized system for proactive fraudsterexposure in a customer service center having multiple service channels.The processor may be configured to: (i) collect call interactions foranalysis from a database of recorded calls in a customer servicechannel. The collecting may be based on predefined rules by a callscollection engine. (ii) store the collected call interactions in aninteraction database; (iii) analyze the call interactions in theinteraction database by a Proactive Fraud Exposure (PFE) engine, saidanalyze comprising: a. generating a voiceprint for each interaction inthe interaction database to be stored in a voiceprints database; b.using machine learning technique to group the call interactions in theinteraction database into one or more clusters, based on respectivevoiceprints in the voiceprints database. Each one of the one or moreclusters is associated with a repeating speaker's voice based on thegenerated voiceprints; c. storing the one or more clusters in a clustersdatabase; and ranking and classifying the one or more clusters stored inthe clusters database to yield a list of potential fraudsters; and (iv)transmit the list of potential fraudsters to an application to displayto a user said list of potential fraudsters via a display unit thus,enabling said user upon review of said list of potential fraudsters toadd fraudsters from said list of potential to a watchlist database andwhen the fraudster calls the customer service center, it may be detectedin real-time.

BRIEF DESCRIPTION OF THE DRAWINGS

In order for the present disclosure, to be better understood and for itspractical applications to be appreciated, the following Figures areprovided and referenced hereafter. It should be noted that the Figuresare given as examples only and in no way limit the scope of thedisclosure. Like components are denoted by like reference numerals.

FIG. 1 schematically illustrates a calls collection engine, inaccordance with some embodiments of the present disclosure;

FIG. 2 schematically illustrates a proactive fraud exposure engine, inaccordance with some embodiments of the present disclosure;

FIG. 3A is a high-level diagram of the system, in accordance with someembodiments of the present disclosure;

FIG. 3B schematically illustrate a system for proactive fraudsterexposure in a customer service center having multiple channels, inaccordance with some embodiments of the present disclosure;

FIG. 4 is a high-level flow diagram depicting clustering algorithm, inaccordance with some embodiments of the present disclosure;

FIGS. 5A-5B schematically illustrate score matrix with speakers markedafter cluster detection and the grouping of the interactions into one ormore clusters, respectively, in accordance with some embodiments of thepresent disclosure;

FIG. 6 is a high-level flow diagram depicting a ranking algorithm, inaccordance with some embodiments of the present disclosure;

FIG. 7 is a high-level flow diagram depicting a method for proactivefraudster exposure, in accordance with some embodiments of the presentdisclosure; and

FIG. 8 is a high-level flow diagram depicting a method for analyzing thecall interactions by a Proactive Fraud Exposure (PFE) engine, inaccordance with some embodiments of the present disclosure;

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the disclosure.However, it will be understood by those of ordinary skill in the artthat the disclosure may be practiced without these specific details. Inother instances, well-known methods, procedures, components, modules,units and/or circuits have not been described in detail so as not toobscure the disclosure.

Although embodiments of the disclosure are not limited in this regard,discussions utilizing terms such as, for example, “processing,”“computing,” “calculating,” “determining,” “establishing”, “analyzing”,“checking”, or the like, may refer to operation(s) and/or process(es) ofa computer, a computing platform, a computing system, or otherelectronic computing device, that manipulates and/or transforms datarepresented as physical (e.g., electronic) quantities within thecomputer's registers and/or memories into other data similarlyrepresented as physical quantities within the computer's registersand/or memories or other information non-transitory storage medium(e.g., a memory) that may store instructions to perform operationsand/or processes. Although embodiments of the disclosure are not limitedin this regard, the terms “plurality” and “a plurality” as used hereinmay include, for example, “multiple” or “two or more”. The terms“plurality” or “a plurality” may be used throughout the specification todescribe two or more components, devices, elements, units, parameters,or the like. Unless explicitly stated, the method embodiments describedherein are not constrained to a particular order or sequence.Additionally, some of the described method embodiments or elementsthereof can occur or be performed simultaneously, at the same point intime, or concurrently. Unless otherwise indicated, use of theconjunction “or” as used herein is to be understood as inclusive (any orall of the stated options).

The term “voiceprint” as used herein refers to a stored sample of avoice of a user which is used to identify and authenticate the user viaspeaker recognition based on characteristics of voice. Thecharacteristics of the voice may be selected from the group consistingof: volume, pace, pitch, resonance, articulation, enunciation,respiration, pauses, timber, stress, rhyme, diction, dialect and thelike.

The term “cluster” as used herein refers to a set of call interactions.

The term “social engineering” as used herein refers to manipulatingagents to provide confidential information to a speaker that pretends tobe a legitimate customer.

The term “i-vector” as used herein refers to intermediate vectors oridentity vectors which is an enhancement for a previously used approachin speaker verification technology called Joint Factor Analysis (JFA).JFA divides a human voice into two factors: a speaker factor and achannel factor. The data structure of the i-vectors may be an array, andeach element in the data structure is representing a characteristic ofthe speech of a speaker. The i-vectors are generated as part ofvoiceprint generation for later on comparison.

The term “similarity score” as used herein refers to a comparison of twovoice samples based on extracted i-vectors.

The term “watchlist” as used herein refers to a list of known fraudsterswhich is commonly saved in a database.

The term “customer service channels” as used herein refers to one typeof channel or more through which a customer service center of anorganization suggests service to its customer. E.g., a customer maycomplete an action with the organization via one of the followingcustomer service channels: Interactive Voice Response (IVR), mobileapplication or speaking with an agent.

The term “threshold” as used herein refers to a scalar such that:

${{Interactions}\mspace{14mu} a\mspace{14mu}{and}\mspace{14mu} b\mspace{14mu}{are}} = \left\{ \begin{matrix}{{mismatch},{{{score}\left( {a,b} \right)} \leq {threshold}}} \\{{match},{{{score}\left( {a,b} \right)} > {threshold}}}\end{matrix} \right.$

The term “claimed customer” as used herein refers to the speaker'sclaimed identity i.e., the details of a legitimate customer, which isprovided by a fraudster in a call interaction between a fraudster and anagent.

Nowadays, organizations must verify customers' identity to protect themand their data from fraud, especially with the rise in identity theftand account takeover, which incur high costs. For that purpose, and alsoto increase the level of security, there are system and methods forauthentication and fraud prevention for customer service channels whichare based on voice biometrics technology and other factors. Biometricstechnology automatically verifies the speaker's claimed identity,commonly, within the first few seconds of a call through naturalconversation with an agent in the customer service channel. Thebiometric technology verifies the identity of the speaker by comparing asample of an ongoing call interaction of the speaker with a voiceprint.

These systems and methods which are based on biometric technology, scanpre-created watchlists against the speaker's voice and callcharacteristics at the beginning of each call to identify suspectedfraud. When a suspected speaker is identified, the systems and methodscan send an alert to the security officers, block the caller fromcommitting a fraud and even block when calls are made in the future,thus lowering overall spending of the organization on authentication.

However, the construction of the watchlists may still require manualchecks and may be time consuming, therefore there is a need for a systemand method that will eliminate the expense and time needed for manualchecks by analyzing the big data of call interactions and extractinginformation related to fraudsters to be later presented to securityofficers, and upon review they will add the fraudsters to the watchlist.

The embodiments taught herein solve the technical problem of checkingand analyzing varied high-volume call interactions which are occurringin high velocity, to detect and identify fraudsters.

The embodiments taught herein relating to call interactions in acustomer call center with call interactions between a customer and anagent i.e., a call center representative is merely shown by way ofexample and technical clarity, and not by way of limitation of theembodiments of the present disclosure. The embodiments herein forproactive fraudster exposure in a customer service channel may beapplied on any customer service channel such as IVR or mobileapplication. Furthermore, the embodiments herein are not limited to acall center but may be applied to any suitable platform providingcustomer service channels.

FIG. 1 schematically illustrates a calls collection engine, inaccordance with some embodiments of the present disclosure.

According to some embodiment, in the customer service center, all callinteractions are recorded and stored in a database of recorded calls. Acalls collection engine 100 receives call interactions from a databaseof recorded calls where some of the calls may be ongoing calls.

According to some embodiments, a user e.g., a security officer maydefine a set of rules which are applied on all call interactions anddetermine which call interactions should be further analyzed. The set ofrules may include various types of rules. For example, (i) The speakergot mismatch result during authentication procedure; (ii) The speakerasked to perform a high-risk transaction; (iii) The agent that handledthe call is associated to a special group that should always bemonitored, e.g., VIP customers. The calls collection engine 100 mayapply predefined rules on the call interactions to extract callinteractions for further analysis i.e., pending interactions to bestored in an interactions database 110, thus lowering the high volume ofcall interactions that must be checked by the security officers. Thepredefined rules may be at least one of: (i) mismatch during customerauthentication; (ii) business data; (iii) agents that are associatedwith a risk group or line of business; (iv) behavioral flows of thespeaker; (v) call content analysis; (vi) frequency of the callinteractions or any combination thereof.

In a non-limiting example, mismatch during customer authentication mayoccur when in the authentication procedure the data that the userprovides does not match the authentication data that is saved in theorganizations database. Further, in a non-limiting example business datamay include high-risk transactions such as money transfer when theorganization is a financial institution. Furthermore, in a non-limitingexample, agents that are associated with a risk group or line ofbusiness may be agents which provide service to VIP customers.Furthermore, in a non-limiting example, behavioral flows of the speaker.

In a non-limiting example, a call content analysis may be related tosearch for keywords and phrases. In another non-limiting example,frequency of the call interactions relates to the number of callinteractions from the same speaker in a predefined time interval.

According to some embodiments, when a call ends its information is sentto a Calls Collection Engine 100 to see if the interaction matches toone or more of the predefined rules of the system. If the callinteraction matches one or more of the rules, it is stored in theinteractions database 110 to be later on analyzed by the PFE enginewhich is shown in detail in FIG. 2.

FIG. 2 schematically illustrates a proactive fraud exposure engine, inaccordance with some embodiments of the present disclosure.

Once a call interaction is stored in interactions database 210 (i.e.,110 in FIG. 1) by the Calls Collection Engine 100 in FIG. 1, the PFEengine 200 may retrieve and read the information of the call interactionfrom the interactions database 210 to analyze it.

According to some embodiments, Calls Collection Engine 100 in FIG. 1 andPFE engine 200 may include a processor, a memory, an output device, aninput device and communication circuitry and interface module for wiredand/or wireless communication with any other computerized device over acommunication network, as illustrated in FIG. 3B, described hereinbelow.

According to some embodiments, in a non-limiting example, the user maybe a security officer and the data may be details of fraudsters to beadded to a watchlist database 240 and the instructions may be the rules,which are applied on all call interactions and determine which callinteractions should be further analyzed.

According to some embodiments, the PFE Engine 200 may use the processorand memory to generate a voiceprint for each call interaction in theinteractions database 210 to be stored in a voiceprints database 220.

Next, according to some embodiments, the PFE Engine 200 may be usingmachine learning technique to group the call interactions in theinteraction database 210 based on the voiceprints database 220 into oneor more clusters which may be stored in a clusters database 230. Eachone of the one or more clusters is associated with a repeating speaker'svoice based on the generated voiceprints.

According to some embodiments, the one or more clusters in the clustersdatabase 230 may be ranked and classified to yield a list of potentialfraudsters.

According to some embodiments, the list of potential fraudsters may betransmitted to an application 260 over a communication network, to belater on displayed to a user via a display unit 250. The user may be asecurity officer that may review the list of potential fraudsters andlisten to the call that is in the respective cluster. Upon reviewal,when the security officer suspects that the call has been made by anactual fraudster, the security officer may add the call and therespective fraudsters information via the application 260 to a watchlistdatabase 240. The application 260 may be web application or desktopapplication.

According to some embodiments, after the details of the fraudster arestored in the watchlist database 240, when the fraudster calls thecustomer service center, it may be detected in real-time. An alert maybe sent to the users i.e., the agents and/or the security officers uponthe detection for further monitoring and analysis or alternatively thecall may be blocked.

FIG. 3A is a high-level diagram of the system, in accordance with someembodiments of the present disclosure.

According to some embodiments, Real Time Authentication (RTA) flows 305may be sent to Real Time Voice Buffering (RTVB) 310 which may bebuffering the call's audio to a Fluent Engine 315. The Fluent Engine 315is a voice biometric engine that is performing authentication and frauddetection. An authentication center 320 holds the fraudsters watchlistsand may forward the watchlists to the Fluent Engine 315. RTA results aretransmitted to a call server 325 which manages all the calls andcontrols the call recording by initiating the call recording in thesystem and the buffering which is performed by RTVB 310. The call server325 also saves all the call-related metadata to the DB server 335, i.e.,once a call ends call-related metadata such as if the call was indeedrecorded and archived, certain business data or having an authenticationmismatch is being saved.

According to some embodiments, Proactive Fraud Engine (PFE) Rule Manager330 which is a sub-component of the call server 325 may tag the relevantPFE calls according to predefined PFE rules. Once a call ends, thetagged PFE calls may be transmitted to a DB Server 335. The DB server335 manages all the call interactions with all the databases which arethe rule database 335 and the voiceprints database such as database 340.

According to some embodiments, PFE call interaction are forwarded todatabase 340 which holds the pending PFE interactions and the PFEvoiceprints. PFE Engine 345 creates the voiceprints from the taggedcalls and performs the clustering algorithms.

According to some embodiments, Storage Center 350 may hold the archivedcalls as Media Files (MF) and may forward MF to PFE Engine 345. PFEEngine 345 may forward clustering result to Rule database 355, whichholds the PFE application data.

PFE application Backend 360 serves the PFE application frontendrequests. PFE Frontend 365 is the application where a user can definerules, review the clustering results, manage them and add new fraudstersto the watchlist database 240 in FIG. 2.

FIG. 3B schematically illustrates a system for proactive fraudsterexposure in a customer service center having multiple channels, inaccordance with some embodiments of the present disclosure.

According to some embodiments, Calls Collection Engine 100 in FIG. 1 andPFE engine 200 may include a processor 3010, a memory 3040, an inputdevice 3025, an output device 3030, and a communication circuitry andinterface module 3005 for wired and/or wireless communication with anyother computerized device over a communication network.

According to some embodiments, the processor 3010 may be configured tooperate in accordance with programmed instructions stored in memory 3040and may include one or more processing units, e.g., of one or morecomputers. The processor 3010 may be further capable of executing anengine such as PFE engine 3020 (also shown in FIG. 2 as 200), forgenerating a voiceprint of a speaker out of an audio sample. Thevoiceprint is stored in a voiceprints database such as voiceprintsdatabase 3035.

According to some embodiments, the processor 3010 via PFE 3020 maycommunicate with an output device such as output device 3030 viaapplication 3060. For example, the output device 3030 may include acomputer monitor or screen and the processor 3010 may communicate with ascreen of the output device 3030. In another example, the output device3030 may include a printer, display panel, speaker, or another devicecapable of producing visible, audible, or tactile output.

According to some embodiments, the processor 3010 via PFE 3020 mayfurther communicate with an input device such as input device 3025 viaapplication 3060. For example, the input device 3025 may include one ormore of a keyboard, keypad or pointing device for enabling a user toinput data or instructions for operation of the processor 3010. In anon-limiting example, the user may be a security officer and the datamay be details of fraudsters to be added to a watchlist database 240 inFIG. 2 and the instructions may be the rules, which are applied on allcall interactions and determine which call interactions in the recordedcalls database 3050 should be stored in interactions database 3045 to befurther analyzed by the PFE engine 3020 (also shown in FIG. 2 as 200).

According to some embodiments, a user may insert the rules according towhich call interactions in the recorded calls database 3050 should bestored in interactions database 3045, via application 3060. In someembodiments, a user may receive a list of potential fraudsters andupdate the watchlist database 240 (FIG. 2) via application 3060 (alsoshown as application 260 in FIG. 2).

According to some embodiments, a calls collection engine such as callcollection engine 3015 (also shown in FIG. 1 as 100) may receive callinteractions from a database of recorded calls such as recorded callsdatabase 3050, where some of the calls may be ongoing calls.

According to some embodiments, the processor 3010 may furthercommunicate with memory 3040. The memory 3040 may include one or morevolatile or nonvolatile memory devices. The memory 3040 may be utilizedto store, for example, programmed instructions for operation of theprocessor 3010, data or parameters for use by the processor 3010 duringoperation, or results of the operation of the processor 3010. Forexample, the memory 3040 may store: recorded calls database 3050, callinteractions in interactions database 3045 (also shown in FIG. 2 as210), voiceprints in voiceprints database 3035 (also shown in FIG. 2 as220) and clusters in a clusters database 3055 (also shown in FIG. 2 as230).

According to some embodiments, the processor 3010 may use PFE engine3020 (also shown in FIG. 2 as 200) to implement machine learningtechnique to group the call interactions in the interaction database3045 into one or more clusters and store the clusters in the clustersdatabase 3055. Each one of the one or more clusters is associated with arepeating speaker's voice based on the generated voiceprints stored inthe voiceprints database 3035.

According to some embodiments, the processor 3010 may further use thePFE engine 3020 to rank and classify the one or more clusters stored inthe clusters database 3055 to yield a list of potential fraudsters.

FIG. 4 is a high-level flow diagram depicting clustering algorithm, inaccordance with some embodiments of the present disclosure. The stepsdescribed herein below may be performed by a processor.

According to some embodiments, operation 410 may comprise taking acollection of call interactions. Operation 420 may comprise, for eachcall interaction, finding the call interactions that are most similar toit and creating a cluster out of them. In some embodiments, clusteringalgorithm 400 may further comprise operation 430, which may comprise, ifthere is no call interaction that is similar to it, creating a clusterof size ‘1’ that represents it. Next, clustering algorithm 400 maycomprise ranking the clusters and determining which clusters have thehighest confidence level.

In some embodiments, clustering algorithm 400 may be illustrated by thefollowing pseudo code:

Given N interactions, and a threshold (T) - init N empty groups (G).Create a NxN matrix (M) containing compare scores of all pairwisecomparisons. Diagonal values should be (−infinity). For i from 0 to N:Find the maximum value for row i, let's say it's in index j if maximum >T: if G[i] and G[j] are both empty - assign them to a new cluster. ifG[i] is empty and G[j] is not - assign G[i] to G[j] (and vice versa). ifG[i] and G[j] are both assigned - merge them. If not: G[i] is assignedto a new cluster

T is determined in the following way:

-   -   Take all the pairwise scores, calculate their mean and variance,        T=mean−Z*variance.    -   Where Z is empirically tested to be from 1 to 2 (commonly 2)    -   Optionally, when detecting extremely large clusters, for example        more than 100 calls in one cluster, repeat all the above for        each large cluster, creating sub-clusters.

FIGS. 5A-5B schematically illustrate score matrix with speakers markedafter cluster detection and the grouping of the interactions into one ormore clusters, respectively, in accordance with some embodiments of thepresent disclosure.

According to some embodiments, in a non-limiting example a score matrixwith speakers marked after cluster detection 510 is shown. In thematrix, given a set of call interactions, there is a pairwise comparisonof all to all, and similarity scores. The similarity scores arecalculated based on i-vectors of each speaker according to a similarityalgorithm.

According to some embodiments, given a threshold, in a non-limitingexample, the threshold value may be ‘25’, all call interactions areclustered together in a set of interactions as shown in 520 (in FIG.5B). If the similarity score of call ‘1’ and call ‘2’ is the highest ina row, then when it is higher than a predefined threshold then that callinteraction is clustered in set of interactions 520.

According to some embodiments, the set of interaction 520 is later ondivided into clusters according to the clustering algorithm 400described in FIG. 4. The result of the clustering algorithm is shown in530.

FIG. 6 is a high-level flow diagram depicting a ranking algorithm 600,in accordance with some embodiments of the present disclosure.

According to some embodiments, in operation 610 the ranking algorithm600 may take all the clusters shown in element 530 in FIG. 5B.

According to some embodiments, operation 620 may comprise, for eachcluster, calculating the confidence of the inner ties, and then inoperation 630 normalizing the calculated confidence to yield a score.

According to some embodiments, the normalization is needed because thematrix includes the speaker effect and the channel effect, and this isalso manifested in the i-vectors themselves, therefore there is a needto later normalize the channel effect.

In some embodiments, operation 640 may comprise checking if it is thelast cluster and operation 650 may comprise storing the cluster ID andthe score in a data structure. In operation 660 this score is used toranking the clusters in the data structure and outputting in a sortedmanner. In a non-limiting example, the sorted clustered may be outputtedin ascendance manner from high to low.

According to some embodiments, the ranking is performed by at least oneof the following approaches: (i) inter-cluster statistics; (ii)probability of representing a fraudster; customers or any combinationthereof.

According to some embodiments, the inter-cluster statistics representthe level of “confidence” that the cluster includes call interactionsthat share the same voice.

According to some embodiments, the probability of representing afraudster may be performed using one or more of the following factors:(i) same voice on same claimed customer also known as “targeted attack”;(ii) same voice on different claimed customer, also known as “spread outattack”; (iii) fraudulent behavioral characteristics of the callinteraction, manifested in the voice such as deception acousticfeatures: stutter, jitter, shimmer and the like, and (iv) metadatarepresenting details of a predefined line of business that is more proneto fraud attacks than others.

According to some embodiments, each factor may be attributed with apredefined weight value and the ranking algorithm 600 may be furtherbased on a weighted average of the factors. The weights may bepredefined in collaboration with the employees in the call center.

In some embodiments, ranking algorithm 600 may be illustrated by thefollowing pseudo code, given N clusters:

Init an empty array A For i from 1 to N: TmpSum = Sum(all pairwisecompares in cluster i) clusterMean = TmpSum/numberOfComparesclusterVariance = variance(all pairwise compares in cluster i)clusterScore = clusterMean/(clusterVariance+1) A.append(clusterScore,i)A = A.sort # based on clusterScore Display to the user ‘y’ highestscored clusters.

FIG. 7 is a high-level flow diagram depicting a method for proactivefraudster exposure 700, in accordance with some embodiments of thepresent disclosure.

In some embodiments, proactive fraudster exposure 700 may compriseoperation 710 for collecting call interactions from a database ofrecorded calls (not shown) in a customer service center having multipleservice channels, whereby the collecting is based on predefined rules bya calls collection engine, e.g., calls collection engine 100 in FIG. 1.

In some embodiments, operation 720 may comprise storing the collectedcall interactions in an interactions database, e.g., interactionsdatabase 110 in FIG. 1.

In some embodiments, operation 730 may comprise analyzing the callinteractions in the interactions database 110 in FIG. 1 by a ProactiveFraud Exposure (PFE) engine, e.g., PFE engine 200 in FIG. 2.

In some embodiments, operation 740 may comprise transmitting the list ofpotential fraudsters to an application, e.g., application 260 in FIG. 2to display to a user, the list of potential fraudsters via a displayunit, e.g., display unit 250 in FIG. 2, thus enabling the user, e.g., asecurity officer to review the list of potential fraudsters and to addfraudsters from the list to a watchlist database, e.g., watchlistdatabase 240 in FIG. 2.

FIG. 8 is a high-level flow diagram depicting a method for analyzing thecall interactions by a Proactive Fraud Exposure (PFE) engine, inaccordance with some embodiments of the present disclosure.

According to some embodiments, operation 730 in FIG. 7 may compriseanalyzing the call interactions by a Proactive Fraud Exposure (PFE)engine. Such operation 730 may comprise operations 800 depicting amethod for analyzing the call interactions by a Proactive Fraud Exposure(PFE) engine. According to some embodiments, operation 810 may comprisegenerating a voiceprint for each call interaction in the interactionsdatabase 210 in FIG. 2 to be stored in a voiceprints database 220 inFIG. 2.

According to some embodiments, operation 820 may comprise using machinelearning technique to group the call interactions in the interactiondatabase, e.g., interaction database 210 in FIG. 2 into one or moreclusters, whereby each one of the one or more clusters is associatedwith a repeating speaker's voice based on the generated voiceprints.

According to some embodiments, operation 830 may comprise storing theone or more clusters in a clusters database, e.g., clusters database 230in FIG. 2. In some embodiments, operation 840 may comprise ranking andclassifying the one or more clusters stored in a clusters database,e.g., clusters database 230 in FIG. 2 to yield a list of potentialfraudsters.

According to some embodiments of the present disclosure, the similarityalgorithm may use a log likelihood ratio, where this ratio is calculatedas follows: given two i-vectors, V1 and V2, assuming V1 and V2 arenormally distributed with mean 0 and variance 1, the ratio may becalculated according to the following calculation:

${{ratio}\left( {{V\; 1},{V\; 2}} \right)} = {{\sum\limits_{i = 1}^{i = n}{V{1\lbrack i\rbrack}^{2}}} - {\sum\limits_{i = 1}^{i = n}{V\;{2\lbrack i\rbrack}^{2}}}}$n may be the length of the i-vector, in a non-limiting example n may beequal to 400.

In some embodiments of the present disclosure, the method may includecalculating the predefined threshold from a decision boundary of adistribution of the similarity scores for voiceprints generated fromspeech data chunks.

It should be understood with respect to any flowchart referenced hereinthat the division of the illustrated method into discrete operationsrepresented by blocks of the flowchart has been selected for convenienceand clarity only. Alternative division of the illustrated method intodiscrete operations is possible with equivalent results. Suchalternative division of the illustrated method into discrete operationsshould be understood as representing other embodiments of theillustrated method.

Similarly, it should be understood that, unless indicated otherwise, theillustrated order of execution of the operations represented by blocksof any flowchart referenced herein has been selected for convenience andclarity only. Operations of the illustrated method may be executed in analternative order, or concurrently, with equivalent results. Suchreordering of operations of the illustrated method should be understoodas representing other embodiments of the illustrated method.

Different embodiments are disclosed herein. Features of certainembodiments may be combined with features of other embodiments; thuscertain embodiments may be combinations of features of multipleembodiments. The foregoing description of the embodiments of thedisclosure has been presented for the purposes of illustration anddescription. It is not intended to be exhaustive or to limit thedisclosure to the precise form disclosed. It should be appreciated bypersons skilled in the art that many modifications, variations,substitutions, changes, and equivalents are possible in light of theabove teaching. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the disclosure.

While certain features of the disclosure have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the disclosure.

What is claimed:
 1. A computer-implemented method for proactivefraudster exposure in a customer service center having multiple servicechannels, the computer-implemented method comprising: collecting callinteractions from a database of recorded calls in a customer servicechannel, wherein the collecting is based on predefined rules by a callscollection engine, storing the collected call interactions in aninteractions database; analyzing the collected call interactions in theinteractions database by a Proactive Fraud Exposure (PFE) engine, saidanalyzing comprising: (i) generating a voiceprint for each collectedcall interaction in the interactions database to be stored in avoiceprints database; (ii) using machine learning technique to group thecollected call interactions in the interaction database into one or moreclusters based on respective voiceprints in the voiceprints database,wherein each one of the one or more clusters is associated with arepeating speaker's voice based on the generated voiceprints; (iii)storing the one or more clusters in a clusters database; and (iv)ranking and classifying the one or more clusters stored in the clustersdatabase to yield a list of potential fraudsters, and transmitting thelist of potential fraudsters to an application to display to a user saidlist of potential fraudsters via a display unit, thus enabling said userto review said list of potential fraudsters and to add fraudsters fromthe list of potential fraudsters to a watchlist database.
 2. Thecomputer-implemented method of claim 1, wherein the generating ofvoiceprints is performed by extracting i-vectors which represents aspeaker effect and a channel effect.
 3. The computer-implemented methodof claim 2, wherein the ranking further includes: (i) comparing eachcall interaction in the interaction database to all other callinteractions in the call interaction database to yield a matrix ofcomparisons; (ii) scoring each pair of call interactions based on theextracted i-vectors; (iii) retrieving from each row in the matrix ofcomparisons a pair of call interactions (i,j) with the higher score; and(iv) for each retrieved pair of call interactions (i,j) performclustering.
 4. The computer-implemented method of claim 3, wherein theclustering is performed according to the following conditions: when thescore of the pair of call interactions (i,j) is higher than a predefinedthreshold: a. when both call interactions (i,j) were not assigned to acluster, assign both interactions to a new cluster; b. when only one ofthe call interactions (i,j) is assigned to a cluster add the callinteraction that is not assigned to the cluster; c. when both callinteractions are assigned merge them to one cluster; when the score ofthe pair of call interactions (i,j) is not higher than a predefinedthreshold: call interaction (i) is assigned to a new cluster.
 5. Thecomputer-implemented method of claim 1, the method further comprisingdetecting fraudsters which are stored on the watchlist database in newcall interactions to the customer service center via one of the multipleservice channels, in real-time.
 6. The computer-implemented method ofclaim 1, wherein the ranking is performed by at least one of: (i)inter-cluster statistics; and (ii) probability of representing afraudster or any combination thereof.
 7. The computer-implemented methodof claim 6, wherein the probability of representing a fraudster iscalculated based on at least one of the following factors: (i) samevoice on same claimed customer; (ii) same voice on different claimedcustomers; (iii) fraudulent behavioral characteristics of the callinteraction, manifested in the voice; (iv) metadata representing detailsof a predefined line of business.
 8. The computer-implemented method ofclaim 7, wherein the method further comprising attributing a predefinedweight value to the factors and the wherein the ranking is further basedon a weighted average of the factors.
 9. The computer-implemented methodof claim 1, wherein the predefined rules are at least one of: (i)mismatch during customer authentication; (ii) business data; (iii)agents that are associated with a risk group or line of business; (iv)behavioral flows of the speaker; (v) call content analysis; and (vi)frequency of the call interactions or any combination thereof.
 10. Thecomputer-implemented method of claim 1, wherein said analyzing isperformed on audio or textual content.
 11. The computer-implementedmethod of claim 1, wherein the collecting is further based on automatedmachine-learning algorithms, such as phonetic speech and voice analysis.12. The computer-implemented method of claim 1, wherein the classifyingcomprises calculating a confidence value for each cluster based on theinner ties between the call interactions in the cluster.
 13. Acomputerized-system for proactive fraudster exposure in a customerservice center having multiple channels, the computerized-systemcomprising: a database of recorded calls; an interactions database; avoiceprints database; a clusters database; a memory to store thedatabase of recorded calls, the interactions database, the voiceprintsdatabase and the clusters database; a display unit; and a processor,said processor is configured to: collect call interactions for analysisfrom the database of recorded calls in a customer service channel,wherein the collecting is based on predefined rules by a callscollection engine, store the collected call interactions in theinteraction database; analyze the collected call interactions in theinteraction database by a Proactive Fraud Exposure (PFE) engine, saidanalyze comprising: a. generating a voiceprint for each collected callinteraction in the interaction database to be stored in a voiceprintsdatabase; b. using machine learning technique to group the collectedcall interactions in the interaction database into one or more clusters,wherein each one of the one or more clusters is associated with arepeating speaker's voice based on the generated voiceprints, c. storingthe one or more clusters in the clusters database; and d. ranking andclassifying the one or more clusters stored in the clusters database toyield a list of potential fraudsters; and transmit the list of potentialfraudsters to an application to display to a user said list of potentialfraudsters via the display unit thus, enabling said user upon review ofsaid list of potential fraudsters to add fraudsters from said list ofpotential to a watchlist database.